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Year : 2011  |  Volume : 2  |  Issue : 3  |  Page : 142-143  

Decoding the miRNAome of peripheral blood mononuclear cells by deep sequencing


School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India

Date of Web Publication26-May-2012

Correspondence Address:
Richa Bharti
School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067
India
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Source of Support: None, Conflict of Interest: None


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How to cite this article:
Bharti R, Bhattacharya A. Decoding the miRNAome of peripheral blood mononuclear cells by deep sequencing. J Nat Sc Biol Med 2011;2, Suppl S1:142-3

How to cite this URL:
Bharti R, Bhattacharya A. Decoding the miRNAome of peripheral blood mononuclear cells by deep sequencing. J Nat Sc Biol Med [serial online] 2011 [cited 2020 Sep 25];2, Suppl S1:142-3. Available from: http://www.jnsbm.org/text.asp?2011/2/3/142/96273

MicroRNAs (miRNAs) are short non-coding RNAs which target specific mRNAs and act as post-transcriptional regulators of cellular machinery. Recent technological advances in gene expression profiling have provided new insights into the biology of miRNAs. Among the modern profiling techniques, deep sequencing technology has a proven edge owing to its ability to detect rare transcripts and condition specific miRNAs besides yielding absolute abundance for each miRNA. The present study was undertaken to identify novel miRNA sequences in the normal peripheral blood mononuclear cells (PBMC) with comparison to the cancerous cells, utilizing deep sequencing technology. We report a detailed miRNA expression profile of PBMCs and two cancerous cell lines K562 and HL60. A comparative analysis of miRNA profiles of normal versus K562 and HL60 cells yielded a specific set of differentially expressed miRNAs. Our results also suggested lower miRNA and DICER population in the K562 cells as compared to PBMCs. Besides this, the target mRNAs obtained by microarray expression profiling showed inverse correlation with the corresponding miRNAs. As a whole, our computational pipeline predicted 370 novel miRNAs and their differential expression with respect to the cancer cell lines. Finally, our results suggest the existence of entirely new clusters or new members in the existing clusters of regulatory miRNAs.




 

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